Refine diffusion graph channels and drop legacy who compatibility
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@@ -20,7 +20,9 @@
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// 阶段 5: Lexical Retrieval + Dense-Gated Event Merge
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// 阶段 6: Floor W-RRF Fusion + Rerank + L1 配对
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// 阶段 7: L1 配对组装（L0 → top-1 AI L1 + top-1 USER L1）
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// 阶段 8: Causation Trace
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// 阶段 7.5: PPR Diffusion
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// 阶段 8: L0 → L2 反向查找（后置，基于最终 l0Selected）
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// 阶段 9: Causation Trace
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// ═══════════════════════════════════════════════════════════════════════════
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import { getAllEventVectors, getChunksByFloors, getMeta, getChunkVectorsByIds } from '../storage/chunk-store.js';
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@@ -1114,6 +1116,7 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
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const eventIndex = buildEventIndex(allEvents);
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let lexicalEventCount = 0;
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let lexicalEventFilteredByDense = 0;
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let l0LinkedCount = 0;
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const focusSetForLexical = new Set((bundle.focusEntities || []).map(normalize));
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for (const eid of lexicalResult.eventIds) {
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@@ -1149,46 +1152,6 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
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lexicalEventCount++;
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}
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// ═══════════════════════════════════════════════════════════════════
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// 阶段 5.5: L0 → L2 反向查找
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// 已召回的 L0 楼层落在某 L2 事件范围内，但该 L2 自身未被召回
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// ═══════════════════════════════════════════════════════════════════
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const recalledL0Floors = new Set(anchorHits.map(h => h.floor));
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let l0LinkedCount = 0;
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for (const event of allEvents) {
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if (existingEventIds.has(event.id)) continue;
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const range = parseFloorRange(event.summary);
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if (!range) continue;
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let hasOverlap = false;
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for (const floor of recalledL0Floors) {
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if (floor >= range.start && floor <= range.end) {
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hasOverlap = true;
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break;
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}
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}
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if (!hasOverlap) continue;
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// 实体分类：与所有路径统一标准
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const participants = (event.participants || []).map(p => normalize(p));
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const hasEntityMatch = focusSetForLexical.size > 0
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&& participants.some(p => focusSetForLexical.has(p));
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const evVec = eventVectorMap.get(event.id);
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const sim = evVec?.length ? cosineSimilarity(queryVector_v1, evVec) : 0;
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eventHits.push({
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event,
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similarity: sim,
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_recallType: hasEntityMatch ? 'DIRECT' : 'RELATED',
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});
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existingEventIds.add(event.id);
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l0LinkedCount++;
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}
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if (metrics) {
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metrics.lexical.eventFilteredByDense = lexicalEventFilteredByDense;
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@@ -1196,14 +1159,10 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
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metrics.event.byRecallType.lexical = lexicalEventCount;
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metrics.event.selected += lexicalEventCount;
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}
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if (l0LinkedCount > 0) {
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metrics.event.byRecallType.l0Linked = l0LinkedCount;
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metrics.event.selected += l0LinkedCount;
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}
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}
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xbLog.info(MODULE_ID,
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`Lexical: chunks=${lexicalResult.chunkIds.length} events=${lexicalResult.eventIds.length} mergedEvents=+${lexicalEventCount} filteredByDense=${lexicalEventFilteredByDense} l0Linked=+${l0LinkedCount} (${lexTime}ms)`
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`Lexical: chunks=${lexicalResult.chunkIds.length} events=${lexicalResult.eventIds.length} mergedEvents=+${lexicalEventCount} filteredByDense=${lexicalEventFilteredByDense} floorFiltered=${metrics.lexical.floorFilteredByDense || 0} (${lexTime}ms)`
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);
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// ═══════════════════════════════════════════════════════════════════
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@@ -1248,7 +1207,56 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
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metrics.timing.diffusion = metrics.diffusion?.time || 0;
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// ═══════════════════════════════════════════════════════════════════
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// 阶段 7: Causation Trace
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// Stage 8: L0 → L2 反向查找（后置，基于最终 l0Selected）
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// ═══════════════════════════════════════════════════════════════════
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const recalledL0Floors = new Set(l0Selected.map(x => x.floor));
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for (const event of allEvents) {
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if (existingEventIds.has(event.id)) continue;
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const range = parseFloorRange(event.summary);
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if (!range) continue;
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let hasOverlap = false;
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for (const floor of recalledL0Floors) {
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if (floor >= range.start && floor <= range.end) {
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hasOverlap = true;
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break;
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}
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}
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if (!hasOverlap) continue;
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// Dense similarity 门槛（与 Lexical Event 对齐）
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const evVec = eventVectorMap.get(event.id);
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const sim = evVec?.length ? cosineSimilarity(queryVector_v1, evVec) : 0;
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if (sim < CONFIG.LEXICAL_EVENT_DENSE_MIN) continue;
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// 实体分类：与所有路径统一标准
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const participants = (event.participants || []).map(p => normalize(p));
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const hasEntityMatch = focusSetForLexical.size > 0
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&& participants.some(p => focusSetForLexical.has(p));
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eventHits.push({
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event,
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similarity: sim,
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_recallType: hasEntityMatch ? 'DIRECT' : 'RELATED',
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});
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existingEventIds.add(event.id);
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l0LinkedCount++;
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}
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if (metrics && l0LinkedCount > 0) {
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metrics.event.byRecallType.l0Linked = l0LinkedCount;
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metrics.event.selected += l0LinkedCount;
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}
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xbLog.info(MODULE_ID,
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`L0-linked events: ${recalledL0Floors.size} floors → ${l0LinkedCount} events linked (sim≥${CONFIG.LEXICAL_EVENT_DENSE_MIN})`
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);
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// ═══════════════════════════════════════════════════════════════════
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// 阶段 9: Causation Trace
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// ═══════════════════════════════════════════════════════════════════
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const { results: causalMap, maxDepth: causalMaxDepth } = traceCausation(eventHits, eventIndex);
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@@ -1288,7 +1296,7 @@ export async function recallMemory(allEvents, vectorConfig, options = {}) {
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console.log(`Fusion (floor, weighted): dense=${metrics.fusion.denseFloors} lex=${metrics.fusion.lexFloors} → cap=${metrics.fusion.afterCap} (${metrics.fusion.time}ms)`);
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console.log(`Floor Rerank: ${metrics.evidence.beforeRerank || 0} → ${metrics.evidence.floorsSelected || 0} floors → L0=${metrics.evidence.l0Collected || 0} (${metrics.evidence.rerankTime || 0}ms)`);
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console.log(`L1: ${metrics.evidence.l1Pulled || 0} pulled → ${metrics.evidence.l1Attached || 0} attached (${metrics.evidence.l1CosineTime || 0}ms)`);
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console.log(`Events: ${eventHits.length} hits, ${causalChain.length} causal`);
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console.log(`Events: ${eventHits.length} hits (l0Linked=+${l0LinkedCount}), ${causalChain.length} causal`);
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console.log(`Diffusion: ${metrics.diffusion?.seedCount || 0} seeds → ${metrics.diffusion?.pprActivated || 0} activated → ${metrics.diffusion?.finalCount || 0} final (${metrics.diffusion?.time || 0}ms)`);
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console.groupEnd();
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